I propose to continue my explorations of parallel distributed processing models of cognitive processes. These models are based on the idea that information processing activity grows out of the interactions of large numbers of simple processing units, and that learning is based on the adjustment of the strengths of the connections among the units. The work is aimed primarily at modeling psychological phenomena in a computationally adequate way, though it draws its inspiration from and is ultimately applicable to our developing understanding of how information processing actually takes place in the brain. Two main areas of research are described, one in the area of language processing and one in the area of network learning mechanisms. In the area of language processing, I plan to carry out a series of studies on sentence comprehension designed to test and elaborate three basic principles of language understanding, and to develop a simulation model based on these principles. In the area of network learning mechanisms, I describe a new network learning algorithm, and describe plans to try to bring the further development of this and related algorithms together with neurophysiological investigations of a) adaptation to changes in environmental input and b) of the laws that govern synaptic change as studied through experiments on long-term potentiation.